Welcome to HKU MSDA Bootcamp 2025 – Data Visualization with R

ggplot2

it starts from the grammar of graphics Wickham (2016)

data
aesthetics
geoms
facets
stats
scales
coordinates
themes

Aesthetics
- specifiy how we want our data to map onto our plot
  - Which variable belongs on the x-axis? What about the y-axis?
  - Are we going to convey additional dimenisons of data with colour, or shape, or opacity?
Scale
- When setting scales, we need to allow for easy data visualisation
- Most of the time we’ll use a linear scale
- but can also use other options such as geometric, or logarithmic, if the data is distributed differently and would better suit these transformations
Geoms
- Geoms are the actual visual elements that we use to represent our data
  - Points, lines, bars, etc.
  - Geoms are the building blocks of our plot
Statistics
- We need to think about summarising our data
- Statistics are used to summarise the data
Facets
- Facets allow us to create multiple plots that each display a subset of the data
- This is useful for comparing different groups or categories within the data

ggplot2

Every ggplot2 plot has three key components:
- data
- A set of aesthetic mappings between variables in the data and visual properties
- At least one layer which describes how to render each observation
  - Layers are usually created with a geom function

ggplot2 - data illustration

Use built-in dataset from ggplot2: mpg
- information about the fuel economy of popular car models in 1999 and 2008
- collected by the US Environmental Protection Agency
- here are some of the variables in the dataset:

Variable	Description
manufacturer	Car manufacturer
model	Car model
year	Year of manufacture
displ	Engine displacement (litres)
hwy	Miles per gallon (highway)
cty	Miles per gallon (city)
cyl	Number of cylinders
drv	Drive type (f = front, r = rear, 4 = 4wd)
class	Type of car
trans	Type of transmission
fl	Fuel type

    + manufacturer, model, year
    + displ (engine displacement in litres)
    + hwy (miles per gallon on the highway)
    + cty (miles per gallon in the city)
    + cyl (number of cylinders)
    + drv (f = front-wheel drive, r = rear wheel drive, 4 = 4wd)
    + class (type of car)
    + trans (type of transmission)
    + fl (fuel type)

The mpg dataset is a tibble, a modern version of a data frame
The mpg dataset is part of the ggplot2 package
The mpg dataset is a tidy dataset
This dataset suggests many interesting questions
- How are engine size and fuel economy related?
- Do certain manufacturers care more about fuel economy than others?
- Has fuel economy improved in the last ten years?
List five functions that you could use to get more information about the mpg dataset
How can you find out what other datasets are included with ggplot2?
Apart from the US, most countries use fuel consumption (fuel consumed over fixed distance) rather than fuel economy (distance travelled with fixed amount of fuel). How could you convert cty and hwy into the European standard of l/100km?
Which manufacturer has the most models in this dataset?
- Which model has the most variations?
- Does your answer change if you remove the redundant specification of drive train (e.g. “pathfinder 4wd”, “a4 quattro”) from the model name?

ggplot2

Let us plot the relationship between engine size and fuel economy

Code

library(ggplot2)

data(mpg)

ggplot(data=mpg, 
       mapping=aes(x=displ, y=hwy)) +
  geom_point()

How would you describe the relationship between displ and hwy?

Code

ggplot(mpg, 
       aes(cty, hwy)) +
  geom_point()

Code

ggplot(diamonds, 
       aes(carat, price)) +
  geom_point()

Code

ggplot(economics, 
       aes(date, unemploy)) +
  geom_line()

Code

ggplot(mpg, 
       aes(cty)) +
  geom_histogram()

ggplot2

Colour, size, shape and other aesthetic attributes

Aesthetics are visual properties of the objects in the plot
- colour, size, shape, linetype, fill, alpha
Aesthetics can be mapped to variables in the data
- aes(colour=variable)
- aes(size=variable)
- aes(shape=variable)
- aes(linetype=variable)
- aes(fill=variable)
- aes(alpha=variable)

ggplot2

Colour, size, shape and other aesthetic attributes

Code

ggplot(mpg, 
       aes(displ, hwy, 
           colour=class)) +
  geom_point()

Code

ggplot(mpg, 
       aes(trans, hwy,
           colour=class)) +
  geom_point()

ggplot2 — labels

Labels are important for making your plot understandable
- xlab() and ylab() functions
- labs() function

Code

ggplot(mpg, 
       aes(displ, hwy)) +
  geom_point(aes(color=class)) +
  labs(x="Engine size (litres)",
       y="Highway fuel economy (miles per gallon)",
       title="Relationship between engine size and fuel economy",
       color="Car type",
       caption="Source: mpg dataset")+
  theme_bw()

ggplot2

ggthemes

Code

library(ggthemes)

ggplot(mpg, 
       aes(displ, hwy)) +
  geom_point(aes(color=class)) +
  labs(x="Engine size (litres)",
       y="Highway fuel economy (miles per gallon)",
       title="Relationship between engine size and fuel economy",
       color="Car type",
       caption="Source: mpg dataset")+
  theme_economist()+
  scale_color_tableau() +
  theme(
    axis.title.x = element_text(margin = margin(t = 10)),
    axis.title.y = element_text(margin = margin(r = 10))
  )

ggplot2 — Facets

Facets allow you to create multiple plots that each display a subset of the data
- facet_wrap() creates a grid of plots
- facet_grid() creates a matrix of plots

Code

ggplot(mpg, 
       aes(displ, hwy)) +
  geom_point() +
  facet_wrap(~class)

ggplot2

Plot geoms

Geoms are the geometric objects that represent the data in the plot
- geom_point() creates a scatterplot
- geom_smooth() creates a smoothed line plot
- geom_histogram() creates a histogram
- geom_boxplot() creates a boxplot
- geom_bar() creates a bar plot
- geom_line() creates a line plot
- geom_vline() adds a vertical line to the plot
- geom_hline() adds a horizontal line to the plot
- geom_abline() adds a diagonal line to the plot

ggplot2

Adding a smoother to a plot

Code

ggplot(mpg, 
       aes(displ, hwy)) +
  geom_point() +
  geom_smooth(span=0.3)

ggplot2 — Boxplots and related plots

Boxplots, violin and jittered points are useful for visualizing the distribution of a continuous variable

Code

library(ggpubr)

boxplot<- ggplot(mpg, 
                 aes(drv, hwy)) +
  geom_boxplot()

violin<- ggplot(mpg, 
                aes(drv, hwy)) + geom_violin()


jitter<- ggplot(mpg, 
                aes(drv, hwy)) +
  geom_jitter()

ggarrange(boxplot, violin, jitter,  ncol=3)

ggplot2 – Boxplots

Code

ggplot(mpg, 
       aes(class, hwy)) +
  geom_boxplot()+
  labs(title="Highway fuel economy by car type",
       x="Car type",
       y="Highway fuel economy (miles per gallon)")+
  coord_flip()+
  theme_economist()

ggplot2 — Bar plots

Bar plots are useful for visualizing the distribution of a categorical variable

Code

ggplot(mpg, 
       aes(class)) +
  geom_bar()

Code

ggplot(mpg, 
       aes(class, fill=drv)) +
  geom_bar()

ggplot2

Histograms and density plots

Histograms and density plots are useful for visualizing the distribution of a continuous variable

Code

ggplot(mpg, 
       aes(hwy)) +
  geom_histogram()

Code

ggplot(mpg, 
       aes(hwy)) +
  geom_density()

ggplot2

Histograms and density plots

Code

den<- ggplot(mpg, aes(displ, colour = drv)) + 
  geom_density(linewidth=0.8)
  
hist<- ggplot(mpg, aes(displ, fill = drv)) + 
  geom_histogram(binwidth = 0.5) + 
  facet_wrap(~drv, ncol = 1)

ggarrange(den, hist, ncol=2)

ggplot2

ggsave - save the graph as an image file

Code

ggsave(filename="mpg_displ.png",width=6, height=4)

Final Example - toy imports to the US from 1996-2005

it is drawn from Scott (2021)

Code

library(tidyverse)

toy_imports <- read_csv("https://raw.githubusercontent.com/kwan-MSDA/Bootcamp_2024/main/dataset/toyimports.csv")

head(toy_imports)

# A tibble: 6 × 8
  partner  year partner_name       product product_name US_report_import pop2000
  <chr>   <dbl> <chr>                <dbl> <chr>                   <dbl>   <dbl>
1 ARE      1998 United Arab Emira…  950341 "Toys repre…             1.06  3.25e6
2 ARE      2000 United Arab Emira…  950349 "Toys repre…            12.0   3.25e6
3 ARE      2003 United Arab Emira…  950349 "Toys repre…             4.65  3.25e6
4 ARE      2005 United Arab Emira…  950320 "Reduced-si…            49.2   3.25e6
5 ARG      1996 Argentina           950341 "Toys repre…             0     3.69e7
6 ARG      1996 Argentina           950310 "Electric t…            10.8   3.69e7
# ℹ 1 more variable: region <dbl>

Task: make a graph showing total toy imports over time for the U.S.’s top 5 trading partners by total dollar value of toys imported

Final Example - toy imports to the US from 1996-2005

Code

country_total<- toy_imports %>% 
  group_by(partner_name) %>%
  summarize(total_import=sum(US_report_import)) %>%
  arrange(desc(total_import)) %>%
  head(5)

country_total

# A tibble: 5 × 2
  partner_name     total_import
  <chr>                   <dbl>
1 China               26842305.
2 Denmark              1034990.
3 Canada                572309.
4 Hong Kong, China      545186.
5 Switzerland           400969.

One More Example - toy imports to the US from 1996-2005

Code

top5_partners=c("China", "Denmark", "Canada", "Hong Kong, China", "Switzerland")

options(scipen = 999)

library(ggthemes)
library(scales)
library(plotly)

p <- toy_imports %>% 
  filter(partner_name %in% top5_partners) %>%
  group_by(year, partner_name) %>%
  summarize(total_import=sum(US_report_import)) %>% 
  ggplot(aes(year, total_import, color=partner_name)) +
  geom_line(size=1.18)+
  labs(title="Toy imports from the U.S.'s top-5 partners, 1996-2005",
       x="Year",
       y="Dollar value of imports (log scale)",
       color="Import Region")+
  scale_x_continuous(breaks=1996:2005)+
  scale_y_log10(
    breaks = trans_breaks("log10", function(x) 10^x),
    labels = trans_format("log10", math_format(.x))) +
    #labels = trans_format("log10", math_format(10^.x))) +
  theme_economist()+ 
  theme(
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5, margin = margin(b = 15)), # Larger, bold, centered title
    axis.title.x = element_text(size = 14, margin = margin(t = 10)), # Larger x-axis label
    axis.title.y = element_text(size = 14, margin = margin(r = 15)), # Larger y-axis label with right margin for spacing
    axis.text = element_text(size = 12), # Larger tick labels
    axis.ticks.y = element_line(color = "black", size = 0.5), # Clearer y-ticks
    axis.ticks.length.y = unit(0.3, "cm"), # Slightly longer y-ticks for prominence
    legend.title = element_text(size = 12), # Larger legend title
    legend.text = element_text(size = 10) # Larger legend text
  )
  


ggplotly(p)

An Example — The five coldest months in Rapid City from 1995 to 2011

Code

library(tidyverse)

rapidcity <- read_csv("https://raw.githubusercontent.com/kwan-MSDA/Bootcamp_2024/main/dataset/rapidcity.csv")

top_5_coldest <- rapidcity %>% 
  group_by(Year, Month) %>%
  summarize(avg_Temp = mean(Temp),
            lowest_temp = min(Temp),
            highest_temp = max(Temp)) %>%
  arrange(avg_Temp) %>%
  round(1) %>% 
  mutate(Month_Year = paste(month.abb[Month], Year, sep="-"), .after = Month) %>%
  head(n=5)

top_5_coldest

# A tibble: 5 × 6
# Groups:   Year [4]
   Year Month Month_Year avg_Temp lowest_temp highest_temp
  <dbl> <dbl> <chr>         <dbl>       <dbl>        <dbl>
1  1996     1 Jan-1996       14.9       -11           46.1
2  2009    12 Dec-2009       16.4        -2.6         35.6
3  2000    12 Dec-2000       17.3        -9           38.8
4  1996    12 Dec-1996       17.5       -10.8         40.4
5  2001     2 Feb-2001       17.6        -3.9         40.8

Bar chart

Code

# Reshape data to long format
top_5_long <- top_5_coldest %>%
  pivot_longer(cols = c(avg_Temp, lowest_temp, highest_temp),
               names_to = "Temp_Type",
               values_to = "Temperature") %>%
  mutate(Temp_Type = factor(Temp_Type, 
                            levels = c("lowest_temp", "avg_Temp", "highest_temp"),
                            labels = c("Lowest", "Average", "Highest")))

# Create the ggplot
p <- ggplot(top_5_long, aes(x = Month_Year, y = Temperature, fill = Temp_Type)) +
  geom_bar(stat = "identity", position = position_dodge(width = 1), alpha = 0.6) +
  scale_fill_manual(values = c("Lowest" = "purple", "Average" = "blue", "Highest" = "red")) +
  labs(title = "Top 5 Coldest Months in Rapid City",
       x = "Month-Year",
       y = "Temperature (°F)",
       fill = "Temperature Type") + # Add legend title
  theme_minimal() +
  theme(
    legend.title = element_text(face = "bold", size = 12, color = "black"), # Style legend title
    legend.text = element_text(size = 10), # Style legend text
    axis.text.x = element_text(angle = 45, hjust = 1) # Rotate x-axis labels
  )

# Convert to interactive plotly plot
ggplotly(p, tooltip = c("x", "y", "fill"))

Code

htmlwidgets::saveWidget(ggplotly(p), "top_5_coldest_months.html")

An Example - Survival on the Titanic

Q: how did survival among adult passengers vary by sex and cabin class?

Code

titanic <- read_csv("https://raw.githubusercontent.com/kwan-MSDA/Bootcamp_2024/main/dataset/titanic.csv")

head(titanic)

# A tibble: 6 × 5
  name                            survived sex       age passengerClass
  <chr>                           <chr>    <chr>   <dbl> <chr>         
1 Allen, Miss. Elisabeth Walton   yes      female 29     1st           
2 Allison, Master. Hudson Trevor  yes      male    0.917 1st           
3 Allison, Miss. Helen Loraine    no       female  2     1st           
4 Allison, Mr. Hudson Joshua Crei no       male   30     1st           
5 Allison, Mrs. Hudson J C (Bessi no       female 25     1st           
6 Anderson, Mr. Harry             yes      male   48     1st

Code

surv_adults<- titanic %>% 
  mutate(Adult = age >= 18) %>%
  filter(Adult) %>%
  group_by(sex, passengerClass) %>%
  summarize(total_count=n(),
            survived = sum(survived=="yes"),
            survival_rate = survived/total_count)


surv_adults

# A tibble: 6 × 5
# Groups:   sex [2]
  sex    passengerClass total_count survived survival_rate
  <chr>  <chr>                <int>    <int>         <dbl>
1 female 1st                    125      121        0.968 
2 female 2nd                     85       74        0.871 
3 female 3rd                    106       47        0.443 
4 male   1st                    144       47        0.326 
5 male   2nd                    143       12        0.0839
6 male   3rd                    289       45        0.156

An Example - Survival on the Titanic

Code

library(ggthemes)

ggplot(surv_adults) +
  geom_col(aes(x=sex, y=survival_rate)) +
  facet_wrap(~passengerClass, nrow=1)+
  labs(title="Survival rate by gender and passenger class",
       y="Survival rate",
       x="gender")+
  theme_economist()

Code

library(ggplot2)
library(ggthemes)
library(scales) # For percent formatting

class_labels <- function(passengerClass) { 
  dplyr::case_when( passengerClass == "1st" ~ "First Class", 
                    passengerClass == "2nd" ~ "Second Class", 
                    passengerClass == "3rd" ~ "Third Class", 
                    TRUE ~ as.character(passengerClass) # Fallback for unexpected values
  ) 
  }
                                                             
ggplot(surv_adults, aes(x = sex, y = survival_rate, fill = sex)) +
  geom_col(position = "dodge", width = 0.45, color = "black", alpha = 0.85) + # Add outline and transparency
  facet_wrap(~passengerClass, nrow = 1, labeller = labeller(passengerClass = class_labels)) + # Clear facet labels
  geom_text(aes(label = scales::percent(survival_rate, accuracy = 1)), 
            vjust = -0.5, size = 4) + # Add percentage labels above bars
  scale_y_continuous(labels = scales::percent_format(accuracy = 1), 
                     limits = c(0, 1), expand = c(0, 0.05)) + # Y-axis as percentage, 0-1 range
  scale_fill_brewer(palette = "Set2") + # Colorblind-friendly palette
  labs(title = "Survival Rate by Gender and Passenger Class",
       y = "Survival Rate",
       x = "Gender") +
  theme_economist() +
  theme( plot.title = element_text(size = 16, face = "bold", hjust = 0.5, margin = margin(b = 15)),
       axis.title.x = element_text(size = 12, margin = margin(t = 10)),
       axis.title.y = element_text(size = 12, margin = margin(r = 10)),
       axis.text.x = element_text(size = 10, margin = margin(t = 5)),
       axis.text.y = element_text(size = 10, margin = margin(r = 5)), 
       strip.text = element_text(size = 11, face = "bold", margin = margin(t = 5, r = 5, b = 10, l = 5)), # Increased bottom margin 
       panel.spacing = unit(1.5, "lines"), # Increased space between facets
       legend.position = "none" )

Extra: Gapminder data

Code

library(gapminder)

data(gapminder)

gapminder %>% 
  group_by(year, continent) %>%
  mutate(median_lifeExp = median(lifeExp)) %>%
  ggplot(aes(year, median_lifeExp, color=continent)) +
  geom_line()+
  labs(title="Life expectancy by continent and year",
       x="Year",
       y="Life expectancy")+
  theme_economist()

Code

ggplot(gapminder, aes(x = continent, y = lifeExp)) +
  geom_boxplot(outlier.colour = "hotpink") +
  geom_jitter(position = position_jitter(width = 0.1, height = 0), alpha = 1 / 4)

Extra: Gapminder data

this is from BBC style

Code

# install.packages('devtools')
#devtools::install_github('bbc/bbplot'))

library(ggpubr)

source("https://raw.githubusercontent.com/kwan-MSDA/R/main/bbc_style.R")

gapminder %>% 
  group_by(year, continent) %>%
  summarize(median_lifeExp = median(lifeExp)) %>%
  ggplot(aes(year, median_lifeExp, color=continent)) +
  geom_line()+
  labs(title="Life expectancy by continent and year",
       x="Year",
       y="Life expectancy")+
  bbc_style()

Extra: Gapminder data

Code

library("ggalt")
library("tidyr")
 
library(gapminder)

dumbbell_df <- gapminder %>%
  filter(year == 1967 | year == 2007) %>%
  select(country, year, lifeExp) %>%
  spread(year, lifeExp) %>%
  mutate(gap = `2007` - `1967`) %>%
  arrange(desc(gap)) %>%
  head(10)
 
#Make plot
ggplot(dumbbell_df, aes(x = `1967`, xend = `2007`, y = reorder(country, gap), group = country)) + 
  geom_dumbbell(colour = "#dddddd",
                size = 3,
                colour_x = "#FAAB18",
                colour_xend = "#1380A1") +
  bbc_style() + 
  labs(title="We're living longer",
       subtitle="Biggest life expectancy rise, 1967-2007")

Extra: Gapminder data

Code

library(hrbrthemes)
library(viridis)

gapminder %>% 
  filter(year==2007) %>%
  mutate(country=factor(country, levels=unique(country))) %>%
  arrange(desc(pop)) %>% 
  ggplot(aes(x=gdpPercap, y=lifeExp, size=pop, fill=continent)) +
  geom_point(alpha=0.6, shape=21, color="black")+
  scale_size(range=c(.1, 24), name="Population (M)")+
  scale_fill_viridis(discrete=TRUE, guide=FALSE, option="A")+
  theme_ipsum()+
  theme(legend.position="none")+
  labs(title="Life expectancy by continent in 2007",
       x="GDP per capita",
       y="Life Expectancy")

Extra: Gapminder data

Code

library(gganimate)

gapminder %>% 
  ggplot(aes(x=gdpPercap, y=lifeExp, size=pop, fill=continent, frame=year)) +
  geom_point(alpha=0.6, shape=21, color="black")+
  scale_size(range=c(.1, 22), name="Population (M)")+
  scale_fill_viridis(discrete=TRUE, guide=FALSE, option="A")+
  theme_ipsum()+
  theme(legend.position="none")+
  labs(title="Life expectancy by continent in {frame_time}",
       x="GDP per capita",
       y="Life Expectancy")+
  geom_text(data=gapminder %>%  filter(pop >1e+8), aes(label=country), size=5, nudge_x=0.1, nudge_y=0.1)+
  transition_time(year)+
  enter_fade()+
  exit_fade()

Code

anim_save("gapminder_gganimate.gif")

Extra: Gapminder data

source

Code

library(plotly)
library(hrbrthemes)
library(viridis)

g<- crosstalk::SharedData$new(gapminder %>% 
                              mutate(country=factor(country, levels=unique(country))) %>%
                              arrange(desc(pop)),
                              ~ continent)
gg<- g %>% 
  ggplot(aes(x=gdpPercap, y=lifeExp, fill=continent, frame=year)) +
  geom_point(aes(size=pop, alpha=0.6, ids=country))+
  scale_size(range=c(.1, 24), name="Population (M)")+
  scale_fill_viridis(discrete=TRUE, guide=FALSE, option="A")+
  scale_alpha(range=c(0.6, 1), guide=FALSE)+
  theme_ipsum()+
  # theme(legend.position="none")+
  labs(title="Life expectancy by continent between 1952-2007",
       x="GDP per capita",
       y="Life Expectancy")

ggplotly(gg, height = 500, width = 800)

References

Imai, Kosuke, and Nora Webb Williams. 2022. Quantitative Social Science : An Introduction in Tidyverse. Princeton, New Jersey: Princeton University Press.

Scott, James. 2021. “Data Science in r: A Gentle Introduction.” 2021. https://bookdown.org/jgscott/DSGI/.

Wickham, Hadley. 2016. Ggplot2 : Elegrant Graphics for Data Analysis. Second edition. Use r! Switzerland: Springer.

———. 2019. Advanced r. Second edition. The r Series. Boca Raton, FL: CRC Press.

Wickham, Hadley, and Garrett Grolemund. 2023. R for Data Science : Import, Tidy, Transform, Visualize, and Model Data. Second edition. Beijing: O’Reilly.